# algorithms.registration.scripting¶

## Module: algorithms.registration.scripting¶

A scripting wrapper around 4D registration (SpaceTimeRealign)

## Functions¶

nipy.algorithms.registration.scripting.aff2euler(affine)

Compute Euler angles from 4 x 4 affine

Parameters: affine : 4 by 4 array An affine transformation matrix The Euler angles associated with the affine
nipy.algorithms.registration.scripting.aff2rot_zooms(affine)

Compute a rotation matrix and zooms from 4 x 4 affine

Parameters: affine : 4 by 4 array An affine transformation matrix R: 3 by 3 array A rotation matrix in 3D zooms: length 3 1-d array Vector with voxel sizes.
nipy.algorithms.registration.scripting.space_time_realign(input, tr, slice_order='descending', slice_dim=2, slice_dir=1, apply=True, make_figure=False, out_name=None)

This is a scripting interface to nipy.algorithms.registration.SpaceTimeRealign

Parameters: input : str or list A full path to a file-name (4D nifti time-series) , or to a directory containing 4D nifti time-series, or a list of full-paths to files. tr : float The repetition time slice_order : str (optional) This is the order of slice-times in the acquisition. This is used as a key into the SLICETIME_FUNCTIONS dictionary from nipy.algorithms.slicetiming.timefuncs. Default: ‘descending’. slice_dim : int (optional) Denotes the axis in images that is the slice axis. In a 4D image, this will often be axis = 2 (default). slice_dir : int (optional) 1 if the slices were acquired slice 0 first (default), slice -1 last, or -1 if acquire slice -1 first, slice 0 last. apply : bool (optional) Whether to apply the transformation and produce an output. Default: True. make_figure : bool (optional) Whether to generate a .png figure with the parameters across scans. out_name : bool (optional) Specify an output location (full path) for the files that are generated. Default: generate files in the path of the inputs (with an _mc suffix added to the file-names. transforms : ndarray An (n_times_points,) shaped array containing nipy.algorithms.registration.affine.Rigid class instances for each time point in the time-series. These can be used as affine transforms by referring to their .as_affine attribute.